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Graph Name Retrieved From View
workflow graph format_rrnas_from_seq_entry

https://github.com/ncbi/pgap.git

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7

workflow graph tt_kmer_compare_wnode

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7

workflow graph allele-process-strain.cwl

https://github.com/datirium/workflows.git

Path: subworkflows/allele-process-strain.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c

workflow graph tt_blastn_wnode

https://github.com/ncbi/pgap.git

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8

workflow graph GSEApy - Gene Set Enrichment Analysis in Python

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

https://github.com/datirium/workflows.git

Path: workflows/gseapy.cwl

Branch/Commit ID: 27bee2c853c98af5ce8ace0585b74658adc2e955

workflow graph Detect Variants workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/detect_variants_mouse.cwl

Branch/Commit ID: 449bc7e45bb02316d040f73838ef18359e770268

workflow graph heatmap-prepare.cwl

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

https://github.com/datirium/workflows.git

Path: subworkflows/heatmap-prepare.cwl

Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b

workflow graph Indices builder from GBOL RDF (TTL)

Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_indexbuilder.cwl

Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41

workflow graph RNA-Seq pipeline single-read stranded mitochondrial

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `samtools sort` to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using `GEEP` reads-counting utility; export results to file

https://github.com/datirium/workflows.git

Path: workflows/rnaseq-se-dutp-mitochondrial.cwl

Branch/Commit ID: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d

workflow graph xenbase-sra-to-fastq-se.cwl

https://github.com/datirium/workflows.git

Path: subworkflows/xenbase-sra-to-fastq-se.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c